-
Notifications
You must be signed in to change notification settings - Fork 481
/
Copy pathcoco.py
179 lines (154 loc) · 7.25 KB
/
coco.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
import copy
import json
import math
import os
import pickle
import cv2
import numpy as np
import pycocotools
from torch.utils.data.dataset import Dataset
BODY_PARTS_KPT_IDS = [[1, 8], [8, 9], [9, 10], [1, 11], [11, 12], [12, 13], [1, 2], [2, 3], [3, 4], [2, 16],
[1, 5], [5, 6], [6, 7], [5, 17], [1, 0], [0, 14], [0, 15], [14, 16], [15, 17]]
def get_mask(segmentations, mask):
for segmentation in segmentations:
rle = pycocotools.mask.frPyObjects(segmentation, mask.shape[0], mask.shape[1])
mask[pycocotools.mask.decode(rle) > 0.5] = 0
return mask
class CocoTrainDataset(Dataset):
def __init__(self, labels, images_folder, stride, sigma, paf_thickness, transform=None):
super().__init__()
self._images_folder = images_folder
self._stride = stride
self._sigma = sigma
self._paf_thickness = paf_thickness
self._transform = transform
with open(labels, 'rb') as f:
self._labels = pickle.load(f)
def __getitem__(self, idx):
label = copy.deepcopy(self._labels[idx]) # label modified in transform
image = cv2.imread(os.path.join(self._images_folder, label['img_paths']), cv2.IMREAD_COLOR)
mask = np.ones(shape=(label['img_height'], label['img_width']), dtype=np.float32)
mask = get_mask(label['segmentations'], mask)
sample = {
'label': label,
'image': image,
'mask': mask
}
if self._transform:
sample = self._transform(sample)
mask = cv2.resize(sample['mask'], dsize=None, fx=1/self._stride, fy=1/self._stride, interpolation=cv2.INTER_AREA)
keypoint_maps = self._generate_keypoint_maps(sample)
sample['keypoint_maps'] = keypoint_maps
keypoint_mask = np.zeros(shape=keypoint_maps.shape, dtype=np.float32)
for idx in range(keypoint_mask.shape[0]):
keypoint_mask[idx] = mask
sample['keypoint_mask'] = keypoint_mask
paf_maps = self._generate_paf_maps(sample)
sample['paf_maps'] = paf_maps
paf_mask = np.zeros(shape=paf_maps.shape, dtype=np.float32)
for idx in range(paf_mask.shape[0]):
paf_mask[idx] = mask
sample['paf_mask'] = paf_mask
image = sample['image'].astype(np.float32)
image = (image - 128) / 256
sample['image'] = image.transpose((2, 0, 1))
del sample['label']
return sample
def __len__(self):
return len(self._labels)
def _generate_keypoint_maps(self, sample):
n_keypoints = 18
n_rows, n_cols, _ = sample['image'].shape
keypoint_maps = np.zeros(shape=(n_keypoints + 1,
n_rows // self._stride, n_cols // self._stride), dtype=np.float32) # +1 for bg
label = sample['label']
for keypoint_idx in range(n_keypoints):
keypoint = label['keypoints'][keypoint_idx]
if keypoint[2] <= 1:
self._add_gaussian(keypoint_maps[keypoint_idx], keypoint[0], keypoint[1], self._stride, self._sigma)
for another_annotation in label['processed_other_annotations']:
keypoint = another_annotation['keypoints'][keypoint_idx]
if keypoint[2] <= 1:
self._add_gaussian(keypoint_maps[keypoint_idx], keypoint[0], keypoint[1], self._stride, self._sigma)
keypoint_maps[-1] = 1 - keypoint_maps.max(axis=0)
return keypoint_maps
def _add_gaussian(self, keypoint_map, x, y, stride, sigma):
n_sigma = 4
tl = [int(x - n_sigma * sigma), int(y - n_sigma * sigma)]
tl[0] = max(tl[0], 0)
tl[1] = max(tl[1], 0)
br = [int(x + n_sigma * sigma), int(y + n_sigma * sigma)]
map_h, map_w = keypoint_map.shape
br[0] = min(br[0], map_w * stride)
br[1] = min(br[1], map_h * stride)
shift = stride / 2 - 0.5
for map_y in range(tl[1] // stride, br[1] // stride):
for map_x in range(tl[0] // stride, br[0] // stride):
d2 = (map_x * stride + shift - x) * (map_x * stride + shift - x) + \
(map_y * stride + shift - y) * (map_y * stride + shift - y)
exponent = d2 / 2 / sigma / sigma
if exponent > 4.6052: # threshold, ln(100), ~0.01
continue
keypoint_map[map_y, map_x] += math.exp(-exponent)
if keypoint_map[map_y, map_x] > 1:
keypoint_map[map_y, map_x] = 1
def _generate_paf_maps(self, sample):
n_pafs = len(BODY_PARTS_KPT_IDS)
n_rows, n_cols, _ = sample['image'].shape
paf_maps = np.zeros(shape=(n_pafs * 2, n_rows // self._stride, n_cols // self._stride), dtype=np.float32)
label = sample['label']
for paf_idx in range(n_pafs):
keypoint_a = label['keypoints'][BODY_PARTS_KPT_IDS[paf_idx][0]]
keypoint_b = label['keypoints'][BODY_PARTS_KPT_IDS[paf_idx][1]]
if keypoint_a[2] <= 1 and keypoint_b[2] <= 1:
self._set_paf(paf_maps[paf_idx * 2:paf_idx * 2 + 2],
keypoint_a[0], keypoint_a[1], keypoint_b[0], keypoint_b[1],
self._stride, self._paf_thickness)
for another_annotation in label['processed_other_annotations']:
keypoint_a = another_annotation['keypoints'][BODY_PARTS_KPT_IDS[paf_idx][0]]
keypoint_b = another_annotation['keypoints'][BODY_PARTS_KPT_IDS[paf_idx][1]]
if keypoint_a[2] <= 1 and keypoint_b[2] <= 1:
self._set_paf(paf_maps[paf_idx * 2:paf_idx * 2 + 2],
keypoint_a[0], keypoint_a[1], keypoint_b[0], keypoint_b[1],
self._stride, self._paf_thickness)
return paf_maps
def _set_paf(self, paf_map, x_a, y_a, x_b, y_b, stride, thickness):
x_a /= stride
y_a /= stride
x_b /= stride
y_b /= stride
x_ba = x_b - x_a
y_ba = y_b - y_a
_, h_map, w_map = paf_map.shape
x_min = int(max(min(x_a, x_b) - thickness, 0))
x_max = int(min(max(x_a, x_b) + thickness, w_map))
y_min = int(max(min(y_a, y_b) - thickness, 0))
y_max = int(min(max(y_a, y_b) + thickness, h_map))
norm_ba = (x_ba * x_ba + y_ba * y_ba) ** 0.5
if norm_ba < 1e-7: # Same points, no paf
return
x_ba /= norm_ba
y_ba /= norm_ba
for y in range(y_min, y_max):
for x in range(x_min, x_max):
x_ca = x - x_a
y_ca = y - y_a
d = math.fabs(x_ca * y_ba - y_ca * x_ba)
if d <= thickness:
paf_map[0, y, x] = x_ba
paf_map[1, y, x] = y_ba
class CocoValDataset(Dataset):
def __init__(self, labels, images_folder):
super().__init__()
with open(labels, 'r') as f:
self._labels = json.load(f)
self._images_folder = images_folder
def __getitem__(self, idx):
file_name = self._labels['images'][idx]['file_name']
img = cv2.imread(os.path.join(self._images_folder, file_name), cv2.IMREAD_COLOR)
return {
'img': img,
'file_name': file_name
}
def __len__(self):
return len(self._labels['images'])